ctc/Changes0000644000175400017540000000205113556116167013655 0ustar00biocbuildbiocbuild18/12/2006 * add references to man pages. 09/11/2005 1.5.1 * new function hclust2treeview 12/07/2005 * a demo and a vignette file 17/06/2005 * example ctc.php for a web application 27/05/2005 * new function read.eisen 08/12/2004: 1.2.8 * minor changes on doc. 16/10/2003: 1.2.5 * Bug fixed for R CMD check. (Examples using Xcluster program cannot be executed). 05/09/2003: 1.2.4 * New release only for Windows. (Just 1.2.3 correctly compiled). 22/07/2003: 1.2.3 * bug fixed with option dec=',' for r2gtr, r2cdt, r2cluster * option colname="ACC" with r2cluster 06/02/2003: 1.2.2 * Major bug fixed on function r2cdt * 2 more parameters on function r2cdt * Bug fixed on function r2gtr 15/01/2003: 1.2.1 * Bug fixed on functions r2gtr and r2cluster * Add links on help files. 22/11/2002: 1.2 * 3 new functions: r2gtr, r2atr, r2cdt * Option dec='.' for functions r2cluster, xcluster2r. 12/11/2002: 1.1-1 * Option description=Boolean in r2xcluster and r2cluster * Fixed some minor bugs (_ remplaced by <- for R 1.6) ctc/DESCRIPTION0000644000175400017540000000126313556146046014073 0ustar00biocbuildbiocbuildPackage: ctc Version: 1.60.0 Date: 2005-11-16 Depends: amap Title: Cluster and Tree Conversion. Author: Antoine Lucas , Laurent Gautier biocViews: Microarray, Clustering, Classification, DataImport, Visualization Maintainer: Antoine Lucas Description: Tools for export and import classification trees and clusters to other programs License: GPL-2 URL: http://antoinelucas.free.fr/ctc git_url: https://git.bioconductor.org/packages/ctc git_branch: RELEASE_3_10 git_last_commit: 85b9683 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 NeedsCompilation: no Packaged: 2019-10-29 23:32:54 UTC; biocbuild ctc/NAMESPACE0000644000175400017540000000016113556116167013601 0ustar00biocbuildbiocbuild export(hc2Newick,hclust2treeview,r2cdt,r2atr,r2gtr,r2cluster,r2xcluster, read.eisen,xcluster,xcluster2r) ctc/R/0000755000175400017540000000000013556116167012565 5ustar00biocbuildbiocbuildctc/R/hc2Newick.R0000644000175400017540000000166413556116167014534 0ustar00biocbuildbiocbuildhc2Newick <- function(hc, flat=TRUE) { dist <- 0 if (is.null(hc$labels)) labels <- seq(along=hc$order) else labels <- hc$labels putparenthesis <- function(i) { ## recursive function ## i: index of row in hc$merge j <- hc$merge[i, 1] k <- hc$merge[i, 2] if (j < 0) { left <- labels[-j] if (k > 0) dist <- hc$height[i] - hc$height[k] else dist <- hc$height[i] } else { left <- putparenthesis(j) } if (k < 0) { right <- labels[-k] if (j > 0) dist <- hc$height[i] - hc$height[j] else dist <- hc$height[i] } else { right <- putparenthesis(k) } if (flat) return(paste("(", left, ":", dist/2, ",", right, ":", dist/2, ")", sep="")) else return(list(left=left, right=right, dist=dist)) } n <- putparenthesis(nrow(hc$merge)) if (flat) n <- paste(n, ";", sep="") return(n) } ctc/R/hclust2treeview.R0000644000175400017540000000147213556116167016053 0ustar00biocbuildbiocbuildhclust2treeview <- function(x,file="cluster.cdt",method = "euclidean", link = "complete",keep.hclust=FALSE) { if("amap" %in% .packages(all=TRUE)) { hr <- hcluster (x,method =method, link=link) hc <- hcluster (t(x),method =method, link=link) } else { hr <- hclust(dist(x,method =method), method=link) hc <- hclust(dist(t(x),method =method), method=link) } basefile = strsplit(file,"\\.")[[1]] if(length(basefile>1)) basefile = paste(basefile[-length(basefile)],collapse=".") r2atr(hc,file=paste(basefile,".atr",sep="")) r2gtr(hr,file=paste(basefile,".gtr",sep="")) r2cdt(hr,hc,x ,file=paste(basefile,".cdt",sep="")) if(keep.hclust) return(list(hr,hc)) else return(1) } ctc/R/r2cdt.R0000644000175400017540000000444513556116167013735 0ustar00biocbuildbiocbuildr2cdt <- function(hr,hc,data,labels=FALSE,description=FALSE,file="cluster.cdt",dec="."){ #------------------------------------------------------- # # Created : 20/11/02 # Last Modified : Time-stamp: <2003-07-22 16:52:28 lucas> # # Description : Write data object to cdt # file (Xcluster or Cluster output). # Should be use with r2gtr # Visualisation of cluster can be # done with tools like treeview # # Author : Antoine Lucas # lucas@toulouse.inra.fr # # Licence : GPL (r2xcluster, not Xcluster) # #------------------------------------------------------- # # n <- dim(data)[1] # Add GWEIGHT column data <- as.data.frame(cbind(1,data)) # If the name column does not exist -> creation # of this column else: put column name on # first place. (column NAME in the file) if(!description){ if(!labels){ data <- cbind(row.names(data),data) } else{ data <- cbind(as.factor(data[,2]),data) } } else{ data <- cbind(as.factor(data[,3]),data[,-3]) } # column YORF in the file if(!labels){ data <- cbind(row.names(data),data) } else{ data <- cbind(as.factor(data[,3]),data[,-3]) } # add GID column GID <- paste ('GENE',0:(n-1),'X',sep='') data <- cbind(as.factor(GID),data) # Put data in right order data <- data [hr$order,] m <- dim(data)[2] data[,5:m] <- data[,5:m][hc$order] colnames(data)[5:m] <- colnames(data)[5:m][hc$order] # Round data data[,5:m] <- signif(data[,5:m], digits = 4) levels(data[,1]) <- c(levels(data[,1]),"1","GID","AID","EWEIGHT") levels(data[,2]) <- c(levels(data[,2]),"1","UNIQID") levels(data[,3]) <- c(levels(data[,3]),"1","NAME") data <- rbind(1,data) nom <- c("GID","UNIQID","NAME","GWEIGHT",names(data)[-c(1,2,3,4)]) names(data) <- nom data[1,1] <- "EWEIGHT" data <- rbind(c("AID",NA,NA,NA,paste('ARRY',hc$order-1,'X',sep='')),data) # NA will be replaced by "" in the file # data[2:length(data[,1]),2] <- NA data[2,2:4] <- NA # Write file if(dec==',') { data<-apply(data,2,function(u){chartr(".",",",u)}) data[data=="NA"]<-NA } write.table(data,file = file,sep = '\t',row.names = FALSE,col.names = TRUE,na="",quote=FALSE) } ctc/R/r2cluster.R0000644000175400017540000000404113556116167014634 0ustar00biocbuildbiocbuildr2cluster <- function(data,labels=FALSE,colname="ACC",description=FALSE,file="cluster.txt",dec='.'){ #------------------------------------------------------- # # Created : 05/07/02 # Last Modified : Time-stamp: <2003-07-22 16:53:00 lucas> # # Description : Write to Cluster file format (Cluster make # Hierarchical cluster analysis) # Author : Antoine Lucas # lucas@toulouse.inra.fr # See Also : r2xcluster # # Xcluster : Cluster is a program that performs # hierarchical clustering, K-means and # SOM. # Cluster is copyrighted. # To get or have information on Cluster: # # http://rana.lbl.gov/EisenSoftware.htm # # Licence : GPL # # #------------------------------------------------------- # # # Example: # source('r2cluster.R') # r2cluster(data) # #------------------------------------------------------- n <- length(data[,1]) data <- as.data.frame(cbind(1,1:n,data)) # If the name column does not exist -> creation # of this column, else: put column name on # first place. if(!description){ if(!labels){ data <- cbind(row.names(data),data) } else{ data <- cbind(as.factor(data[,3]),data) } } else{ data <- cbind(as.factor(data[,4]),data[,-4]) } if(!labels){ data <- cbind(row.names(data),data) } else{ data <- cbind(as.factor(data[,4]),data[,-4]) } levels(data[,1]) <- c(levels(data[,1]),"1",colname,"EWEIGHT") levels(data[,2]) <- c(levels(data[,2]),"1","NAME","EWEIGHT") data <- rbind(1,data) nom <- c(colname,"NAME","GWEIGHT","GORDER",names(data)[-c(1,2,3,4)]) names(data) <- nom data[1,1] <- "EWEIGHT" data[1,2:4] <- NA # NA will be replaced by "" in the file #data[2:length(data[,1]),2] <- NA #data[2,3] <- NA # Write file if(dec==',') { data<-apply(data,2,function(u){chartr(".",",",u)}) data[data=="NA"]<-NA } write.table(data,file,sep='\t',row.names = FALSE,col.names = TRUE,na="",quote=FALSE) } ctc/R/r2gtr.R0000644000175400017540000000555113556116167013756 0ustar00biocbuildbiocbuildr2gtr <- function(hr,file="cluster.gtr",distance=hr$dist.method,dec=".", digits=5) { #------------------------------------------------------- # # Created : 20/11/02 # Last Modified : Time-stamp: <2005-04-05 16:06:45 lucas> # # Description : Write hclust object to gtr atr # files (Xcluster or Cluster output). # Visualisation of cluster can be # done with tools like treeview # Author : Antoine Lucas # lucas@toulouse.inra.fr # See Also : plot.hclust (library mva) # # Licence : GPL #------------------------------------------------------- height <- hr$height if(substr(distance,1,1)=="p") { height <- 1 - height } else { height <- height +1 height <- height[1] / height # height <- (( 2 / height ) -1 ) / (( 2 / height[1] ) -1) } height <- signif(height, digits = digits) n <- length(height) node <- 1:n node <- paste ('NODE',node,'X',sep='') merge1 <- hr$merge[,1] merge11 <- paste ('NODE',merge1,'X',sep='') merge12 <- paste ('GENE',-1-merge1,'X',sep='') merge1[hr$merge[,1]>0] <- merge11[hr$merge[,1]>0] merge1[hr$merge[,1]<0] <- merge12[hr$merge[,1]<0] merge2 <- hr$merge[,2] merge11 <- paste ('NODE',merge2,'X',sep='') merge12 <- paste ('GENE',-1-merge2,'X',sep='') merge2[hr$merge[,2]>0] <- merge11[hr$merge[,2]>0] merge2[hr$merge[,2]<0] <- merge12[hr$merge[,2]<0] data <- data.frame(cbind(node,merge1,merge2)) data <- cbind(data,height) write.table(data,file=file,row.name=FALSE,col.names=FALSE,quote=FALSE,sep='\t',dec=dec) } #----------------------------------- # Cosmetic modifications for r2atr #----------------------------------- r2atr <- function(hc,file="cluster.atr",distance=hc$dist.method,dec=".", digits=5) { height <- hc$height if(substr(distance,1,1)=="p") { height <- 1 - height } else { height <- height +1 height <- height[1] / height } height <- signif(height, digits = digits) n <- length(height) node <- 1:n node <- paste ('NODE',node,'X',sep='') merge1 <- hc$merge[,1] merge11 <- paste ('NODE',merge1,'X',sep='') merge12 <- paste ('ARRY',-1-merge1,'X',sep='') merge1[hc$merge[,1]>0] <- merge11[hc$merge[,1]>0] merge1[hc$merge[,1]<0] <- merge12[hc$merge[,1]<0] merge2 <- hc$merge[,2] merge11 <- paste ('NODE',merge2,'X',sep='') merge12 <- paste ('ARRY',-1-merge2,'X',sep='') merge2[hc$merge[,2]>0] <- merge11[hc$merge[,2]>0] merge2[hc$merge[,2]<0] <- merge12[hc$merge[,2]<0] data <- data.frame(cbind(node,merge1,merge2)) data <- cbind(data,height) if(dec==',') { data<-apply(data,2,function(u){chartr(".",",",u)}) data[data=="NA"]<-NA } write.table(data,file=file,row.name=FALSE,col.names=FALSE,quote=FALSE,sep='\t') } ctc/R/r2xcluster.R0000644000175400017540000000403113556116167015023 0ustar00biocbuildbiocbuildr2xcluster <- function(data,labels=FALSE,description=FALSE,file="xcluster.txt"){ #------------------------------------------------------- # # Created : 10/12/01 # Last Modified : Time-stamp: <2002-11-12 13:17:15 lucas> # # Description : Write to Xcluster file format (Xcluster make # Hierarchical cluster analysis) # Author : Antoine Lucas # lucas@toulouse.inra.fr # See Also : xcluster2r # # Xcluster : Xcluster is a C program that performs # hierarchical clustering, K-means and # SOM. # Xcluster is copyrighted. # To get or have information on Xcluster: # # http://genome-www.stanford.edu/~sherlock/cluster.html # # Licence : GPL (r2xcluster, not Xcluster) # #------------------------------------------------------- # # # Example: # source('r2xcluster.R') # r2xcluster(data) # system('Xcluster -f xcluster.txt') # h <- xcluster2r('xcluster.gtr') # library(mva) # plot(h,hang=-1) # #------------------------------------------------------- data <- as.data.frame(cbind(1,data)) # If the name column does not exist -> creation # of this column else: put column name on # first place. if(!description){ if(!labels){ data <- cbind(row.names(data),data) } else{ data <- cbind(as.factor(data[,2]),data) } } else{ data <- cbind(as.factor(data[,3]),data[,-3]) } if(!labels){ data <- cbind(row.names(data),data) } else{ data <- cbind(as.factor(data[,3]),data[,-3]) } levels(data[,1]) <- c(levels(data[,1]),"1","NAME","EWEIGHT") levels(data[,2]) <- c(levels(data[,2]),"1","DESCRIPTION") data <- rbind(1,data) nom <- c("NAME","DESCRIPTION","GWEIGHT",names(data)[-c(1,2,3)]) data <- rbind(nom,data) data[2,1] <- "EWEIGHT" # NA will be replaced by "" in the file #data[2:length(data[,1]),2] <- NA data[2,2:3] <- NA # Write file write.table(data,file,sep='\t',row.names = FALSE,col.names = FALSE,na="",quote=FALSE) } ctc/R/read.eisen.R0000644000175400017540000000167613556116167014737 0ustar00biocbuildbiocbuildread.eisen <- function(file,sep="\t",dec=".",format.check=TRUE) { f<-read.table(file=file,header=TRUE,sep=sep,dec=dec) rownames(f)<-f[,1] if (format.check & colnames(f)[1] %in% c("UNIQID","YORF","MCLID","CLID","ACC")) { stop(paste("This is not a proper Eisen-formatted file ('",file,"').",sep="")) } if ("NAME"==colnames(f)[2]) { colstart<-3 } else { colstart<-2 } if (""==rownames(f)[1]) { rowstart<-2 } else { rowstart<-1 } r<-f[rowstart:nrow(f),colstart:ncol(f)] if (2==rowstart) { attr(r,"second.row")<-f[1,colstart:ncol(f)] } else { attr(r,"second.row")<-NULL } if (3==colstart) { attr(r,"NAME")<-f[rowstart:nrow(f),2] } else { attr(r,"NAME")<-NULL } return(r) } ctc/R/xcluster.R0000644000175400017540000000626113556116167014566 0ustar00biocbuildbiocbuildxcluster <- function(data,distance="euclidean",clean=FALSE,tmp.in="tmp.txt",tmp.out="tmp.gtr"){ #------------------------------------------------------- # # Created : 10/12/01 # Last Modified : Time-stamp: <2002-11-12 09:48:10 lucas> # # Description : Execute Xcluster (Xcluster make # Hierarchical cluster analysis) # Author : Antoine Lucas # lucas@toulouse.inra.fr # See Also : xcluster2r # # Xcluster : Xcluster is a C program that performs # hierarchical clustering, K-means and # SOM. # Xcluster is copyrighted. # To get or have information on Xcluster: # # http://genome-www.stanford.edu/~sherlock/cluster.html # # Licence : GPL (r2xcluster, not Xcluster) # # #------------------------------------------------------- # Xcluster flags #------------------------------------------------------- # # -g 0|1|2 ; 0 indicates no gene clustering, 1 indicates non-centered metric, 2 indicates centered metric when clustering genes. 2 is default. # -e 0|1|2 ; 0 indicates no experiment clustering, see above for 1 and 2. 0 is the default. # -p 0|1 ; whether to use pearson correlation (1), or Euclidean distance (0). 1 is the default. # -s 0|1 ; whether to make a SOM (1). 0 is the default. # -x specify x dimension of SOM # -y specify y dimension of SOM # -r 0|1 ; whether to seed the random number generator with the time when making a SOM. 1 is the default. # -k num ; how many k-means clusters to make. num is an integer, greater than 1, and preferably less than a gazillion. # -l 0|1 ; whether to log transform the data. # -u string ; a unique identifier by which to name the output files, instead of basing their names on the input file. eg -u 888 will generate 888.cdt as an output file. #------------------------------------------------------- # # # Example: # source('r2xcluster.R') # system('Xcluster -f xcluster.txt') # h <- xcluster2r('xcluster.gtr') # library(mva) # plot(h,hang=-1) # #------------------------------------------------------- r2xcluster(data,file=tmp.in) #------------------------------------- # CASE DISTANCE=EUCLIDEAN #------------------------------------- if(substr(distance,1,1)=="e") { script <- paste ("Xcluster -f",tmp.in," -e 0 -p 0 -s 0 -l 0") system(script) tree <- xcluster2r(file=tmp.out,distance="euclidean",labels=TRUE,fast= TRUE,clean=clean) } #------------------------------------- # CASE DISTANCE=PEARSON #------------------------------------- if(substr(distance,1,1)=="p") { script <- paste ("Xcluster -f",tmp.in,"-g 2 -e 0 -p 1 -s 0 -l 0") system(script) tree <- xcluster2r(file=tmp.out,distance="pearson",labels=TRUE,fast= TRUE,clean=clean) } #------------------------------------- # CASE DISTANCE=NONCENTEREDPEARSON #------------------------------------- if(substr(distance,1,1)=="n") { script <- paste ("Xcluster -f",tmp.in,"-g 1 -e 0 -p 1 -s 0 -l 0") system(script) tree <- xcluster2r(file=tmp.out,distance="pearson",labels=TRUE,fast= TRUE,clean=clean) } script <- paste ("rm ",substr(tmp.in,0,nchar(tmp.in)-3),"*",sep='') system(script) tree } ctc/R/xcluster2r.R0000644000175400017540000001250513556116167015030 0ustar00biocbuildbiocbuildxcluster2r <- function(file,distance="euclidean",labels=FALSE,fast=FALSE,clean=FALSE,dec="."){ #------------------------------------------------------- # # Created : 26/11/01 # Last Modified : Time-stamp: <2002-11-22 11:48:29 lucas> # # Description : Read Xcluster output (Xcluster make # Hierarchical cluster analysis) to # analyse and plot the result. # Author : Antoine Lucas # lucas@toulouse.inra.fr # See Also : plot.hclust (library mva) # # Xcluster : Xcluster is a C program that performs # hierarchical clustering, K-means and # SOM. # Xcluster is copyrighted. # To get or have information on Xcluster: # # http://genome-www.stanford.edu/~sherlock/cluster.html # # Licence : GPL (xcluster2r, not Xcluster) # # #------------------------------------------------------- # # # Example: # source('xcluster2r.R') # h <- xcluster2r('data.gtr') # library(mva) # plot(h,hang=-1) # # #------------------------------ # Determin if it is a .gtr or # .atr file #------------------------------ ext <- substr(file,nchar(file)-2,nchar(file)-2) if( (ext=='a') || (ext=='A') ) { premierelettre <- 'A' atr <- TRUE } else { premierelettre <- 'G' atr <- FALSE } #------------------------------ # reading file #------------------------------ if(atr){ premierelettre <- 'A' } else { premierelettre <- 'G' } data <- read.table(file,sep='\t',dec=dec) data1 <- as.character(data[,2]) i1 <- (substr(data1,1,1)==premierelettre)*(-1) #si i1 -1 => Gene, #Si i1 0 => Cluster data1 <- as.integer(substr(data1,5,nchar(data1)-1)) data1 <- data1*i1+i1+data1*(1+i1) data2 <- as.character(data[,3]) i2 <- (substr(data2,1,1)==premierelettre)*(-1) data2 <- as.integer(substr(data2,5,nchar(data2)-1)) data2 <- data2*i2+i2+data2*(1+i2) if(substr(distance,1,1)=="e") { Hheight <- 2 / (data[,4] +1 ) #Distance = Euclidean } else{ Hheight <- 1-data[,4] #Distance = pearson (centered or not) } rm(data) #---------------------------------------------------- # Giving ordered distances #---------------------------------------------------- if(clean){ Hheight <- cummax(Hheight) } #---------------------------------------------------- # Reorganizing data (Not essential, # but gives the same look as R) # Fast=T skip this part #---------------------------------------------------- if(!fast){ for(i in 1:length(data1)){ if(abs(data1[i])>abs(data2[i])){ tmp <- data2[i] data2[i] <- data1[i] data1[i] <- tmp} if(data1[i]>0 && data2[i]<0) { tmp2 <- data2[i] data2[i] <- data1[i] data1[i] <- tmp2 } } } #---------------------------------------------------- # Order the clusters #---------------------------------------------------- liste <- list("1"=c(data1[1],data2[1])) for(i in 2:length(data1)){ if(data1[i]<0 && data2[i]<0){ liste[[as.character(i)]] <- c(data1[i],data2[i]) } if(data1[i]>0 && data2[i]<0) { tmp <- as.character(data1[i]) liste[[as.character(i)]] <- c(data2[i],liste[[tmp]]) liste[[tmp]] <- c() } if(data1[i]<0 && data2[i]>0) { tmp <- as.character(data2[i]) liste[[as.character(i)]] <- c(data1[i],liste[[tmp]]) liste[[tmp]] <- c() } if(data1[i]>0 && data2[i]>0) { tmp1 <- as.character(data1[i]) tmp2 <- as.character(data2[i]) if(data1[i]<=data2[i]) { liste[[as.character(i)]] <- c(liste[[tmp1]],liste[[tmp2]]) } else { liste[[as.character(i)]] <- c(liste[[tmp2]],liste[[tmp1]]) } liste[[tmp1]] <- c() liste[[tmp2]] <- c() } } #-------------------------------- # Giving the outputs #-------------------------------- Hmerge <- cbind(as.integer(data1),as.integer(data2)) Horder <- -liste[[as.character(length(data2))]] Hlabels <- as.character(1:(length(data2)+1)) Hmethod <- "average" #Hdist.method <- "Pearson" #Hcall <- "Xcluster" #-------------------------------- # Getting labels on .cdt file #-------------------------------- if(labels){ if(atr) { filetxt <- paste(substr(file,0,nchar(file)-4),'.txt',sep='') data <- read.table(filetxt,nrows=1,sep='\t',dec=dec) Hlabels <- as.character(t(data[4:length(data)])) } else { filecdt <- paste(substr(file,0,nchar(file)-4),'.cdt',sep='') data <- read.table(filecdt,skip=1,sep='\t',dec=dec) data1 <- as.character(data[,1]) data1 <- as.integer(substr(data1,5,nchar(data1)-1)) data2 <- as.character(data[,2]) Hlabels[data1+1] <- data2 } } tree <- list(merge=Hmerge,height=Hheight,order=Horder,labels=Hlabels,method=Hmethod) class(tree) <- "hclust" tree } ctc/R/zzz.R0000755000175400017540000000023013556116167013543 0ustar00biocbuildbiocbuild.noGenerics <- TRUE .conflicts.OK <- TRUE .onLoad <- .First.lib <- function(lib, pkg) { if("amap" %in% .packages(all=TRUE)) require("amap") } ctc/build/0000755000175400017540000000000013556146046013462 5ustar00biocbuildbiocbuildctc/build/vignette.rds0000644000175400017540000000031213556146046016015 0ustar00biocbuildbiocbuildb```b`fcd`b2 1# 'fO.I +G+)O)M.S(W*æ % Ml ba DX%Z]?4-ީE0=(jؠjX2sRad9.nP&c0Gq?gQ~`nݣ9JI,IK+)ctc/demo/0000755000175400017540000000000013556116167013310 5ustar00biocbuildbiocbuildctc/demo/00Index0000644000175400017540000000002413556116167014436 0ustar00biocbuildbiocbuildctc Ctc demo file ctc/demo/ctc.R0000755000175400017540000000265513556116167014217 0ustar00biocbuildbiocbuildcat ("\n\n C T C \n D E M O \n\n") cat("\n------ Standard clustering with R (not ctc): -------\n") data(USArrests) h = hclust(dist(USArrests)) plot(h) readline("Next") cat("\n------ Get the ``heatmap'': -------\n") heatmap(as.matrix(USArrests)) readline("Next") cat("\n------ BUILDING HIERARCHICAL CLUSTERING WITH ANOTHER SOFTWARE -------\n") cat("\n------ Write data table to Xcluster file format -------\n") r2xcluster(USArrests,file='USArrests_xcluster.txt') readline("Next") cat("\n------ Write data table to cluster file format -------\n") r2cluster(USArrests,file='USArrests_cluster.txt') readline("Next") cat("\n------ Hierarchical clustering (need Xcluster tool by Gavin Sherlock) -------\ntry:\n\nh.xcl=xcluster(USArrests)\nplot(h.xcl) ") readline("Next") hr = hclust(dist(USArrests)) hc = hclust(dist(t(USArrests))) cat("\n------ USING OTHER VISUALIZATION SOFTWARES -------\n") cat("\n------ Export hclust objects to Newick format files -------\n") write(hc2Newick(hr),file='hclust.newick') readline("Next") cat("\n------ Export hclust objects to Freeview or Treeview visualization softwares -------\n") r2atr(hc,file="cluster.atr") r2gtr(hr,file="cluster.gtr") r2cdt(hr,hc,USArrests ,file="cluster.cdt") readline("Next") cat("\n------ Clustering and Export hclust objects to Freeview or Treeview visualization softwares -------\n") hclust2treeview(USArrests,file="cluster.cdt") ctc/exec/0000755000175400017540000000000013556116167013310 5ustar00biocbuildbiocbuildctc/exec/ctc.php0000644000175400017540000001057613556116167014603 0ustar00biocbuildbiocbuild
Ctc Demo Web application V0-1

This demo makes hierarchical clustering on your data

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A first Column
A first line

$MAX_FILE) exit("File size: $taillefichier; max allowed: $MAX_FILE"); } /* ================================== */ /* We write R code in file $ID/prog.R */ /* ================================== */ $fp=fopen("$ID/prog.R",'w'); fwrite($fp,"library(ctc)\n"); /* Read Data */ fwrite($fp,"data <- read.delim('$ID/data.txt',header=$HEADER,row.names=$ROWNAMES) \n" ); fwrite($fp,"data \n"); /* Hierarchical clustering */ fwrite($fp,"hr <- hclust(dist(data)) \n"); fwrite($fp,"hc <- hclust(dist(t(data))) \n"); fwrite($fp,"dr <- as.dendrogram(hr) \n"); fwrite($fp,"dc <- as.dendrogram(hc) \n"); /* A pdf file */ fwrite($fp,"pdf(file='$ID/Rplots.pdf') \n"); fwrite($fp,"heatmap(as.matrix(data),Colv=dc,Rowv=dr) \n"); fwrite($fp,"dev.off() \n"); /* Some png images */ fwrite($fp,"bitmap(file='$ID/heatmap.png') \n"); fwrite($fp,"heatmap(as.matrix(data),Colv=dc,Rowv=dr) \n"); fwrite($fp,"dev.off() \n"); fwrite($fp,"r2atr(hc,file='$ID/cluster.atr') \n"); fwrite($fp,"r2gtr(hr,file='$ID/cluster.gtr') \n"); fwrite($fp,"r2cdt(hr,hc,data ,file='$ID/cluster.cdt') \n"); fclose($fp); /* ===================== */ /* Send command (R batch)*/ /* ===================== */ system("$R_BIN --no-save < $ID/prog.R > $ID/prog.R.out 2> $ID/prog.R.warnings"); /* ===================================== */ /* We create html page including results */ /* ===================================== */ echo "

Ctc Demo results

"; echo "A pdf file

"; echo "3 files for Freeview"; echo " or Treeview: "; echo "cdt "; echo "atr "; echo "gtr "; echo "

"; echo "

"; /* Signature */ echo "

This results made by ctc package. Code use: prog.R, Out: prog.R.out, Warnings: prog.R.warnings."; } ?>

ctc/inst/0000755000175400017540000000000013556146046013340 5ustar00biocbuildbiocbuildctc/inst/doc/0000755000175400017540000000000013556146046014105 5ustar00biocbuildbiocbuildctc/inst/doc/ctc.R0000644000175400017540000000317113556146046015003 0ustar00biocbuildbiocbuild### R code from vignette source 'ctc.Rnw' ################################################### ### code chunk number 1: ctc.Rnw:45-48 ################################################### data(USArrests) h = hclust(dist(USArrests)) plot(h) ################################################### ### code chunk number 2: ctc.Rnw:51-52 ################################################### heatmap(as.matrix(USArrests)) ################################################### ### code chunk number 3: ctc.Rnw:61-63 ################################################### library(ctc) r2xcluster(USArrests,file='USArrests_xcluster.txt') ################################################### ### code chunk number 4: ctc.Rnw:66-67 ################################################### r2cluster(USArrests,file='USArrests_xcluster.txt') ################################################### ### code chunk number 5: ctc.Rnw:92-94 ################################################### hr = hclust(dist(USArrests)) hc = hclust(dist(t(USArrests))) ################################################### ### code chunk number 6: ctc.Rnw:101-102 ################################################### write(hc2Newick(hr),file='hclust.newick') ################################################### ### code chunk number 7: ctc.Rnw:106-109 ################################################### r2atr(hc,file="cluster.atr") r2gtr(hr,file="cluster.gtr") r2cdt(hr,hc,USArrests ,file="cluster.cdt") ################################################### ### code chunk number 8: ctc.Rnw:113-114 ################################################### hclust2treeview(USArrests,file="cluster.cdt") ctc/inst/doc/ctc.Rnw0000644000175400017540000000732113556116167015352 0ustar00biocbuildbiocbuild% -*- mode: noweb; noweb-default-code-mode: R-mode; -*- % building this document: (in R) Sweave ("ctc.Rnw") \documentclass[a4paper]{article} \title{Ctc Package} \author{Antoine Lucas} \SweaveOpts{echo=FALSE} %\usepackage{a4wide} %\VignetteIndexEntry{Introduction to ctc} %\VignettePackage{ctc} \usepackage{url} \begin{document} \maketitle \tableofcontents \section{Overview} {\tt Ctc} package provides several functions for conversion. Specially to export and import data from Xcluster\footnote{\url{http://genome-www.stanford.edu/~sherlock/cluster.html}} or Cluster\footnote{http://rana.lbl.gov/EisenSoftware.htm} software (very used for Gene's expression analysis), and to export clusters to TreeView or Freeview visualization software. \section{Aim} \begin{itemize} \item To explore clusters made by Xcluster and Cluster . \item To cluster data with Xcluster (it requires very low memory usage) and analyze the results with R. Warning: results are not exactly the same as hclust results with R. \end{itemize} \section{Usage} Standard way of building a hierarchical clustering with R is with this command: %<>= <>= data(USArrests) h = hclust(dist(USArrests)) plot(h) @ Or for the ``heatmap'': <>= heatmap(as.matrix(USArrests)) @ \subsection{Building hierarchical clustering with another software} We made these tools \begin{description} \item[r2xcluster] Write data table to Xcluster file format <>= library(ctc) r2xcluster(USArrests,file='USArrests_xcluster.txt') @ \item[r2cluster] Write data table to Cluster file format <>= r2cluster(USArrests,file='USArrests_xcluster.txt') @ \item[xcluster] Hierarchical clustering (need Xcluster tool by Gavin Sherlock) \begin{verbatim} > h.xcl=xcluster(USArrests) > plot(h.xcl) \end{verbatim} It is roughtly the same as \begin{verbatim} > r2xcluster(USArrests,file='USArrests_xcluster.txt') > system('Xcluster -f USArrests_xcluster.txt -e 0 -p 0 -s 0 -l 0') > h.xcl=xcluster2r('USArrests_xcluster.gtr',labels=TRUE) \end{verbatim} \item[xcluster2r] Importing Xcluster/Cluster output \end{description} \subsection{Using other visualization softwares} We now consider that we have an object of the type produced by 'hclust' (or a hierarchical cluster imported with previous functions) like: <>= hr = hclust(dist(USArrests)) hc = hclust(dist(t(USArrests))) @ \begin{description} \item[hc2Newick] Export hclust objects to Newick format files <>= write(hc2Newick(hr),file='hclust.newick') @ \item[r2gtr,r2atr,r2cdt] Export hclust objects to Freeview or Treeview visualization softwares <>= r2atr(hc,file="cluster.atr") r2gtr(hr,file="cluster.gtr") r2cdt(hr,hc,USArrests ,file="cluster.cdt") @ \item[hclust2treeview] Clustering and Export hclust objects to Freeview or Treeview visualization softwares <>= hclust2treeview(USArrests,file="cluster.cdt") @ \end{description} \section{See Also} Theses examples can be tested with command {\tt demo(ctc)}.\\ \noindent All functions has got man pages, try {\tt help.start()}.\\ \noindent Ctc aims to interact with other softwares, some of them: \begin{description} \item[xcluster] made by Gavin Scherlock, http://genome-www.stanford.edu/\~\/sherlock/cluster.html \item[Cluster, Treeview] made by Michael Eisen, http://rana.lbl.gov/EisenSoftware.htm \item[Freeview] made by Marco Kavcic and Blaz Zupan, http://magix.fri.uni-lj.si/freeview \end{description} \noindent If you want to cite amap or ctc in a publication, use~: Antoine Lucas and Sylvain Jasson, \emph{Using amap and ctc Packages for Huge Clustering}, R News, 2006, vol 6, issue 5 pages 58-60. \end{document} ctc/inst/doc/ctc.pdf0000644000175400017540000045167313556146046015371 0ustar00biocbuildbiocbuild%PDF-1.5 % 4 0 obj << /Length 1507 /Filter /FlateDecode >> stream xڥWK6WT"Z$ER  [(4i $9xmzXv͡,J pz2e2md\L#a,|tIj3Jg1ɇYZU_iU:gPS΅(5*Jò.6)Sd$UdzeEW/*r~$|p~e^[`{P]ܡ^[Rۨ-0 mO-Ag^??Sj!^!s$~߂Gc_ܑ[g(pM,@Jz <aAH“5 krn*-ȹ(Ak׆,Pk5G1 XH`pVwˎ|MR"'U`'!~_D1@ LFk]c6jaD6%:4rJ{|ԗII8ӎT# /څR:?s~H]RrK׍;Ӭp2ʊ|~ 0-d7h#GBnOvՈm) ?ۖZԁpi$_f)!ݱ Boo|PycՖȺ{{/+YWùqKN*5:XV endstream endobj 21 0 obj << /Length 916 /Filter /FlateDecode >> stream xWN@}W7{ *R*5ơwfv7RBK:{̙}4OixkOȈ\z0!7y_AKOᏡe6涿vcn4VYƭkhShΡkkG UoaOxh$|KTGҥ%wN{}?:aR%LDZrd\#^ ݢ_ڟ`@ 3g3ܑ9?^W!{8=1c4XFtJax럦sp4e* `[2!@wAFh/\LǢaF26a\tt U3(AwGنۂɮeנõuL㒋rz`8&֛3++>-"^DTV9[ߍ'd́ԚC뤮z9>WP אqRY㈟vBy Ary=L`T PTI!៭q;j9b-w+k<$/5fDD/n&ƚ{(bS涗D. _0; 5/u\ĥ S(}pWm @N\<2)*;Dno./gV53s3m2d |(#f*:#!5iOD}zK\̾, }^oESr監u$a|GM%VI*2΄&7^[ endstream endobj 1 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmptbtIK6/Rbuild96e74472b6a/ctc/vignettes/ctc-002.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 24 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 25 0 R>> /ExtGState << >>/ColorSpace << /sRGB 26 0 R >>>> /Length 3131 /Filter /FlateDecode >> stream xv|^ Җs۱ /X3#q8ӧP s (j^6y|,S;tMv]c;џ}Ssbv2?K]~U׮`e5S74ק+?vqJA˦usV vX2Uآ۩/퇌rZsr {_Wup))TKY))Rr.zȯ4_ۥ+AXDc[ycFH FsK Ѕ < Q,c13.͎ Roum"AzR֐JT!mAj3Hi3i)fGѸe]y6'ncڶw4^˼oW_ʷW0ypFn(o*[9|eP.SI-NsEYCQCY -,eSY#ZCOe=xA&=-褣$3((QT?Oeg/b=Y.=((ʚITMOeTMn:ZHOE5TCVg$ʊHTDOeMT3آ#eZV)(QYEGY1},%̈́A/QuA35M65Y35CQ35CQ3,hfVGJJTLOe$*:B&ajȚ Sh#j1eEYI}NIusZGTȪ e2V}/Z\oGVj;݆qõj+h{*k|u 𤋮DE'DK@T)öP%ۑkz;rEQoG({*k\;ѶM2>\.c`fgF 6քQ6&ÿ796`·z?ݝF0Ս9xھ6Tx>=mT1nQtӘ_o!pϰܽ =)Ii116׊t{E.{%la̰ذY~w8oL JWX P,^.c" #}} 6G2ۺMp9LwsWf03\':e̿ ǭVPoW< 8ic0}rx8ݚV6?Gڷe}{W]F_qGj0/^ ̫ߚ[_ys+A[m~4P1?4GSb%0cgXln|h|"P{}lf'Hi쫺MpDsy9c`}xMD~-k+܌f4M>`թ8ϡkhsFc ^$cQdk?`|æ@5u)]{:nB<#i4x}3l5 2ʮNffkj7hDxh驔+an%Qf} 0[CCBp4n8aGc8|@h|̃sk +9G" cu9}exo2n 8>y0Ǜ@<"O`G<_{iB "xt d_0n X_h'dLƭ_҇s4uJⱿ}ø5?Q_ q'q4CB`:׈s/^_"C'MO9Ȁ?<`ϥ8G?籿}Ǹ5P M97pn )@? 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